Course Description

Learn how to train, manage, and deploy machine learning models on Azure. The course focuses on Azure Machine Learning, exploring the service and features like assessing data, managing compute, tracking the training machine learning models, implementing Responsible AI principles, and deploying models to endpoints.

Work with notebook and scripts to train machine learning models and use Azure Machine Learning managed compute for workloads.

Students are expected to be familiar with the basic data science and machine learning concepts.

This course covers the objectives for Microsoft Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

University of Calgary is Microsoft Education Global Training Partner.

Course Details

Learning Outcomes

By completion of this course, successful students will be able to:

  • Understand the Azure Machine Learning workspace resources and assets.
  • Provision an Azure Machine Learning workspace through the Azure portal or Azure CLI.
  • Create and manage data assets and datastores within the workspace using the Python SDK.
  • Create and manage compute resources within the workspace using the Python SDK.
  • Train a machine learning model with the no-code Designer in the Azure Machine Learning Studio.
  • Use Automated Machine Learning to explore featurization and algorithms.
  • Train and track a machine learning model in a notebook in the Azure Machine Learning workspace.
  • Train and track a machine learning model using scripts in the Azure Machine Learning workspace.
  • Create and schedule Azure Machine Learning pipelines.
  • Deploy a machine learning model to a real-time endpoint.
  • Deploy a machine learning model to a batch endpoint.
  • Apply Responsible AI principles to data, models, and model training.
  • Monitor data and models.


  • Getting Started with Azure Machine Learning
  • No-Code Machine Learning
  • Running Experiments and Training Models
  • Working with Data
  • Working with Compute
  • Orchestrating Machine Learning Workflows
  • Deploying and Consuming Models
  • Training Optimal Models
  • Responsible Machine Learning
  • Monitoring Models


This course includes hands-on activities to reinforce the concepts taught and provide a practical learning experience.

Lab access will be provided at no additional cost.


No mandatory prerequisite.  

Self-assessment for enrolment: 

A minimum of 6 months relevant working experience and knowledge in: 

  • Basic data science and machine learning concepts 
  • Training and deployment of machine learning models using Notebook 


Recommended prerequisites: 

  • ICT 779 Python for Data Analysis 
  • DAT 120 Practical Machine Learning for Business 

Applies Towards the Following Program(s)

Enrol Now - Select a section to enrol in
Online Synchronous
T, Th
6:00PM to 9:00PM
Sep 17, 2024 to Oct 17, 2024
Schedule and Location
Delivery Options
Course Fees
Flat Fee non-credit $979.00
Required Software
This course includes extensive hands-on activities designed to help you learn by working with Azure. To complete the labs in this course, you will need: A modern web browser such as Microsoft Edge Microsoft Azure Lab Access (will be provided at no additional cost)
Reading List / Textbook
No Textbook Required. 
Section Notes

Classes are held online in real time (Mountain Time) at the specified time and dates.

Students will require access to a computer with the required software, Internet connection, a headset with speakers and microphone, webcam, and a monitor large enough to display multiple applications (or the use of two monitors).

This course uses Desire2Learn (D2L), an online learning management system, and Microsoft Teams or Zoom web conferencing software. The instructor will post the course outline and other materials in D2L. For more information, please visit our Online Learning Resources.

Unless notified, all online courses are available at 9 am MT the day before the start date. Students registered on (or after) the start date will receive access within one day of registration.

Students unfamiliar with online learning are encouraged to take our free Digital Skills for Learning Online course.

Unless otherwise stated, notice of withdrawal or transfer from a course must be received at least seven calendar days prior to the start date of the course.

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